Analysis of SLAM-Based Path Planning Strategies for Mobile Robots
DOI:
https://doi.org/10.62051/1zt43n48Keywords:
Mobile Robot; SLAM technology; path planning.Abstract
Simultaneous localization and mapping, (SLAM), is universally utilized in mobile robot systems to enable mobile robots to achieve accurate perception and decision-making. Aiming at reviewing the development and research progress of SLAM technology, this review introduces academic research findings on the front-end, back-end, and path planning strategies of SLAM systems focusing on fundamental principles of SLAM systems. Followed by discussion focusing on technology such as semantic maps, deep learning, etc., which are regarded as current research hotspot, the review brings out a brief outlook on future development of SLAM system. Through analysis of current SLAM technology, the review aims to not only summarize challenges current SLAM systems face, but also to offer some guidance. The review, which begins with its discussion from basic process of SLAM technology, highlights the fundamental role SLAM systems play in precise navigation and rapid path planning of the mobile robot systems. Systematically summarizing existing solutions in complex scenarios, it provides a reference for future research and engineering applications.
Downloads
References
[1] Al-Tawil B, Hempel T, Abdelrahman A, et al. A review of visual SLAM for robotics: Evolution, properties, and future applications. Frontiers in Robotics and AI, 2024, 11: 1347985.
[2] Zhang Y, Shi P, Li J. 3D LiDAR SLAM: A survey. The Photogrammetric Record, 2024, 39(186): 457–517.
[3] Sarlin P-E, DeTone D, Malisiewicz T, et al. SuperGlue: Learning feature matching with graph neural networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020: 4938–4947.
[4] Lindenberger P, Sarlin P-E, Rocco I, et al. LightGlue: Local feature matching at light speed. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023: 1361–1371.
[5] Tyszkiewicz M, Fua P, Trulls E. DISK: Learning local features with policy gradient. Advances in Neural Information Processing Systems (NeurIPS), 2020, 33: 14254–14265.
[6] Sun J, Shen Z, Wang Y, et al. LoFTR: Detector-free local feature matching with transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021: 8922–8931.
[7] Wang T, Lin J, Li S. MatchFormer: Interleaving attention in transformers for feature matching. Proceedings of the British Machine Vision Conference (BMVC), 2022.
[8] Kaess M, Johannsson H, Roberts R, et al. iSAM2: Incremental smoothing and mapping using the Bayes tree. The International Journal of Robotics Research, 2012, 31(2): 216–235.
[9] Zhang Y, Huang J, Wang H, et al. MR-iSAM2: Incremental smoothing and mapping with multi-root Bayes tree for multi-robot SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021: 5090–5096.
[10] Mur-Artal R, Tardós J D. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, 2017, 33(5): 1255–1262.
[11] Kim G, Kim A, Kim A. Scan Context++: Structural place recognition robust to rotation and lateral variations in urban environments. IEEE Transactions on Robotics, 2021, 37(6): 1858–1871.
[12] Xiang H, Shi W, Fan W, et al. FastLCD: A fast and compact loop closure detection approach using 3D point cloud for indoor mobile mapping. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102409.
[13] Liu Y, Zhang W, Wu Z, et al. Real-Time Lidar Odometry and Mapping with Loop Closure. Sensors, 2022, 22(3): 1046.
[14] Hu Y, Xie F, Yang J, et al. Efficient path planning algorithm based on laser SLAM and an optimized visibility graph for robots. Remote Sensing, 2024, 16(16): 2938.
[15] Blöchliger F, Fehr M, Dymczyk M, et al. Topomap: Topological mapping and navigation based on visual SLAM maps. arXiv preprint arXiv:1709.05533, 2017.
[16] Wang Z, Zhang Q, Li J, et al. A computationally efficient semantic SLAM solution for dynamic scenes. Remote Sensing, 2019, 11(11): 1363.
[17] Alqobali R, Alnasser R, Rashidi A, et al. A real-time semantic map production system for indoor robot navigation. Sensors, 2024, 24(20): 6691.
[18] Khan S, Niaz A, Dou Y, et al. Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization. Frontiers in Neurorobotics, 2025, 18: 1428358.
[19] Li D, Shi X, Long Q, et al. DXSLAM: A robust and efficient visual SLAM system with deep features. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
[20] Sun R-H, Zhao X, Wu C-D, et al. Research on mobile robot navigation method based on semantic information. Sensors, 2024, 24(13): 4341.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Transactions on Computer Science and Intelligent Systems Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
